2020
DOI: 10.1109/jeds.2020.2995710
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Optimization on Adhesive Stamp Mass-Transfer of Micro-LEDs With Support Vector Machine Model

Abstract: In this work, the process of adhesive stamp mass-transfer of micro light-emitting diode (micro-LED) is optimized by a Support Vector Machine (SVM) model. The pickup experiments have been performed repeatedly for hundreds of times from which the separation speed and the force between the stamp and the donor substrate are extracted as signal features. The SVM model with a Gaussian kernel function is designed to classify pickup results into success and failure. In addition, the optimal cost parameter C as well as… Show more

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Cited by 17 publications
(10 citation statements)
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“…[88] In 2020, Lu et al optimized the stamp transfer technology by utilizing the support vector model. [95] The placement rate of ≈1 M units h −1 was achieved using a stamp size of 150 mm. This is suitable for a small area or finer micro-LEDs display applications but not enough for large area displays.…”
Section: Assembling Technologies For Mass Production Of Micro-leds Di...mentioning
confidence: 99%
“…[88] In 2020, Lu et al optimized the stamp transfer technology by utilizing the support vector model. [95] The placement rate of ≈1 M units h −1 was achieved using a stamp size of 150 mm. This is suitable for a small area or finer micro-LEDs display applications but not enough for large area displays.…”
Section: Assembling Technologies For Mass Production Of Micro-leds Di...mentioning
confidence: 99%
“…The transfer cycle created higher throughput and accuracy than the typical pick-and-place technique. [127] Many mass transfer technologies have been introduced thus far, including elastomer stamp, [115][116][117][118] laser-based, [119,120] fluidic-based assembly, [121,122] electrostatic, [123] and roll-to-roll techniques. [124,125] A typical process flow of elastomer stamp transfer printing is shown in Figure 10a.…”
Section: Reliability and Cost Issuementioning
confidence: 99%
“…This problem was resolved by the development of a KSVM with a kernel trick applied to an SVM. The KSVM is a technique for obtaining a separation interface at an existing dimension through linear separation using the SVM after mapping data in a high dimension using a kernel trick [17].…”
Section: Kernel Support Vector Machinementioning
confidence: 99%